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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
参考链接:
https://www.thepythoncode.com/article/pretraining-bert-huggingface-transformers-in-python
https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py
"""
import argparse
from itertools import chain
import os
from pathlib import Path
import platform
from datasets import Dataset, DatasetDict, IterableDataset, load_dataset
import torch
from transformers.data.data_collator import DataCollatorForLanguageModeling
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.trainer import Trainer
from transformers.training_args import TrainingArguments
from project_settings import project_path
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--pretrained_model_name_or_path",
default=(project_path / "pretrained_models/gpt2-chinese-cluecorpussmall").as_posix(),
type=str
)
parser.add_argument("--train_subset", default="train.jsonl", type=str)
parser.add_argument("--valid_subset", default="valid.jsonl", type=str)
parser.add_argument("--output_dir", default="serialization_dir", type=str)
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument("--evaluation_strategy", default="no", choices=["no", "steps", "epoch"], type=str)
parser.add_argument("--per_device_train_batch_size", default=8, type=int)
parser.add_argument("--gradient_accumulation_steps", default=4, type=int)
parser.add_argument("--learning_rate", default=1e-5, type=float)
parser.add_argument("--weight_decay", default=0, type=float)
parser.add_argument("--max_grad_norm", default=1.0, type=float)
parser.add_argument("--num_train_epochs", default=3.0, type=float)
parser.add_argument("--max_steps", default=-1, type=int)
parser.add_argument("--lr_scheduler_type", default="cosine", type=str)
parser.add_argument("--warmup_ratio", default=0.0, type=float)
parser.add_argument("--warmup_steps", default=3000, type=int)
parser.add_argument("--logging_steps", default=300, type=int)
parser.add_argument("--save_strategy", default="steps", type=str)
parser.add_argument("--save_steps", default=500, type=int)
parser.add_argument("--save_total_limit", default=3, type=int)
parser.add_argument("--no_cuda", action="store_true")
parser.add_argument("--seed", default=3407, type=str, help="https://arxiv.org/abs/2109.08203")
# parser.add_argument("--fp16", action="store_true")
parser.add_argument("--fp16", action="store_false")
parser.add_argument("--half_precision_backend", default="auto", type=str)
parser.add_argument("--dataloader_num_workers", default=5, type=int)
parser.add_argument("--disable_tqdm", action="store_false")
parser.add_argument("--remove_unused_columns", action="store_false")
# parser.add_argument("--deepspeed", default="ds_z3_config.json", type=str)
parser.add_argument("--deepspeed", default=None, type=str)
parser.add_argument("--optim", default="adamw_hf", type=str)
parser.add_argument("--report_to", default="tensorboard", type=str)
parser.add_argument("--resume_from_checkpoint", default=None, type=str)
# parser.add_argument("--gradient_checkpointing", action="store_true")
parser.add_argument("--gradient_checkpointing", action="store_false")
parser.add_argument("--truncate_longer_samples", action="store_true")
# parser.add_argument("--truncate_longer_samples", action="store_false")
parser.add_argument("--max_seq_length", default=1024, type=int)
args = parser.parse_args()
return args
def main():
args = get_args()
# dataset
dataset_dict = DatasetDict()
train_data_files = [args.train_subset]
dataset_dict["train"] = load_dataset(
path="json", data_files=[str(file) for file in train_data_files]
)["train"]
valid_data_files = [args.valid_subset]
dataset_dict["valid"] = load_dataset(
path="json", data_files=[str(file) for file in valid_data_files]
)["train"]
print(dataset_dict)
# model
tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_name_or_path)
model = GPT2LMHeadModel.from_pretrained(args.pretrained_model_name_or_path)
def encode_with_truncation(examples):
outputs = tokenizer.__call__(examples['text'],
truncation=True,
padding='max_length',
max_length=args.max_seq_length,
return_special_tokens_mask=True)
return outputs
def encode_without_truncation(examples):
outputs = tokenizer.__call__(examples['text'],
return_special_tokens_mask=True)
return outputs
def group_texts(examples):
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
if total_length >= args.max_seq_length:
total_length = (total_length // args.max_seq_length) * args.max_seq_length
result = {
k: [t[i: i + args.max_seq_length] for i in range(0, total_length, args.max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
if args.truncate_longer_samples:
dataset_dict = dataset_dict.map(
encode_with_truncation,
batched=True,
drop_last_batch=True,
keep_in_memory=False,
# num_proc=None if platform.system() == 'Windows' else os.cpu_count() // 2,
num_proc=None,
)
dataset_dict.set_format(type="torch", columns=["input_ids", "attention_mask"])
else:
dataset_dict = dataset_dict.map(
encode_without_truncation,
batched=True,
drop_last_batch=True,
keep_in_memory=False,
# num_proc=None if platform.system() == 'Windows' else os.cpu_count() // 2,
num_proc=None,
)
dataset_dict.set_format(type="torch", columns=["input_ids", "attention_mask"])
dataset_dict = dataset_dict.map(
group_texts,
batched=True,
drop_last_batch=True,
keep_in_memory=False,
# num_proc=None if platform.system() == 'Windows' else os.cpu_count() // 2,
num_proc=None,
)
dataset_dict.set_format("torch")
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=False
)
training_args = TrainingArguments(
output_dir=args.output_dir,
overwrite_output_dir=args.overwrite_output_dir,
evaluation_strategy=args.evaluation_strategy,
per_device_train_batch_size=args.per_device_train_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps,
learning_rate=args.learning_rate,
num_train_epochs=args.num_train_epochs,
max_steps=args.max_steps,
lr_scheduler_type=args.lr_scheduler_type,
warmup_steps=args.warmup_steps,
logging_steps=args.logging_steps,
save_steps=args.save_steps,
save_total_limit=args.save_total_limit,
no_cuda=args.no_cuda,
fp16=args.fp16,
half_precision_backend=args.half_precision_backend,
# deepspeed=args.deepspeed,
report_to=args.report_to,
resume_from_checkpoint=args.resume_from_checkpoint,
gradient_checkpointing=args.gradient_checkpointing,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset_dict["train"],
)
train_result = trainer.train()
# 保存最好的 checkpoint
final_save_path = os.path.join(training_args.output_dir, "final")
trainer.save_model(final_save_path) # Saves the tokenizer too
# 保存训练指标
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
tokenizer.save_pretrained(final_save_path)
return
if __name__ == '__main__':
main()
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